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Numpy Data Type Serialization Using Msgpack

Package Description

This package provides encoding and decoding routines that enable the serialization and deserialization of numerical and array data types provided by numpy using the highly efficient msgpack format. Serialization of Python's native complex data types is also supported.

Latest VersionBuild Status

Installation

msgpack-numpy requires msgpack-python and numpy.

To install the package and all dependencies. You can download the source tarball, unpack it, and run

python setup.py install

from within the source directory.

Usage

The easiest way to use msgpack-numpy is to call its monkey patching function after importing the Python msgpack package:

import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization routines (and other packages that use them) to become numpy-aware. Of course, one can also manually pass the encoder and decoder provided by msgpack-numpy to the msgpack routines:

import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.
If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON variable is set), it will fall back to using the slower pure Python msgpack implementation.

Notes

The primary design goal of msgpack-numpy is ensuring preservation of numerical data types during msgpack serialization and deserialization. Inclusion of type information in the serialized data necessarily incurs some storage overhead; if preservation of type information is not needed, one may be able to avoid some of this overhead by writing a custom encoder/decoder pair that produces more efficient serializations for those specific use cases.

Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as a fallback solution to enable msgpack-numpy to handle such arrays. As the additional overhead of pickle serialization negates one of the reasons to use msgpack, it may be advisable to either write a custom encoder/decoder to handle the specific use case efficiently or else not bother using msgpack-numpy.

Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied if they are to be modified.

Development

The latest source code can be obtained from GitHub.

msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.

Install tox to support testing across multiple python versions in your development environment. If you use conda to install python use tox-conda to automatically manage testing across all supported python versions.

# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:

tox

Authors

See the included AUTHORS.md file for more information.

License

This software is licensed under the BSD License. See the included LICENSE.md file for more information.

About

Serialize numpy arrays using msgpack

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GitHub - opentensor/msgpack-numpy: Serialize numpy arrays using msgpack · GitHub
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Numpy Data Type Serialization Using Msgpack
Package Description
This package provides encoding and decoding routines that enable the
serialization and deserialization of numerical and array data types provided by
numpy using the highly efficient
msgpack format. Serialization of Python's
native complex data types is also supported.
Latest VersionBuild Status
Installation
msgpack-numpy requires msgpack-python and numpy.
To install the package and all dependencies. You can download
the source tarball, unpack it, and run
python setup.py install

from within the source directory.
Usage
The easiest way to use msgpack-numpy is to call its monkey patching
function after importing the Python msgpack package:
import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization
routines (and other packages that use them) to become numpy-aware.
Of course, one can also manually pass the encoder and
decoder provided by msgpack-numpy to the msgpack routines:
import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.

If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON
variable is set), it will fall back to using the slower pure Python msgpack
implementation.
Notes
The primary design goal of msgpack-numpy is ensuring preservation of numerical
data types during msgpack serialization and deserialization. Inclusion of type
information in the serialized data necessarily incurs some storage overhead; if
preservation of type information is not needed, one may be able to avoid some
of this overhead by writing a custom encoder/decoder pair that produces more
efficient serializations for those specific use cases.
Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as
a fallback solution to enable msgpack-numpy to handle
such arrays. As the additional overhead of pickle serialization negates one
of the reasons to use msgpack, it may be advisable to either write a custom
encoder/decoder to handle the specific use case efficiently or else not bother
using msgpack-numpy.
Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied
if they are to be modified.
Development
The latest source code can be obtained from GitHub.
msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.
Install tox to support testing
across multiple python versions in your development environment. If you
use conda to install python use
tox-conda to automatically manage
testing across all supported python versions.
# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:
tox

Authors
See the included AUTHORS.md file for
more information.
License
This software is licensed under the BSD License.
See the included LICENSE.md file for
more information.
About
Serialize numpy arrays using msgpack
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
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GitHub - opentensor/msgpack-numpy: Serialize numpy arrays using msgpack · GitHub
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Repository files navigation
Numpy Data Type Serialization Using Msgpack
Package Description
This package provides encoding and decoding routines that enable the
serialization and deserialization of numerical and array data types provided by
numpy using the highly efficient
msgpack format. Serialization of Python's
native complex data types is also supported.
Latest VersionBuild Status
Installation
msgpack-numpy requires msgpack-python and numpy.
To install the package and all dependencies. You can download
the source tarball, unpack it, and run
python setup.py install

from within the source directory.
Usage
The easiest way to use msgpack-numpy is to call its monkey patching
function after importing the Python msgpack package:
import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization
routines (and other packages that use them) to become numpy-aware.
Of course, one can also manually pass the encoder and
decoder provided by msgpack-numpy to the msgpack routines:
import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.

If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON
variable is set), it will fall back to using the slower pure Python msgpack
implementation.
Notes
The primary design goal of msgpack-numpy is ensuring preservation of numerical
data types during msgpack serialization and deserialization. Inclusion of type
information in the serialized data necessarily incurs some storage overhead; if
preservation of type information is not needed, one may be able to avoid some
of this overhead by writing a custom encoder/decoder pair that produces more
efficient serializations for those specific use cases.
Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as
a fallback solution to enable msgpack-numpy to handle
such arrays. As the additional overhead of pickle serialization negates one
of the reasons to use msgpack, it may be advisable to either write a custom
encoder/decoder to handle the specific use case efficiently or else not bother
using msgpack-numpy.
Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied
if they are to be modified.
Development
The latest source code can be obtained from GitHub.
msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.
Install tox to support testing
across multiple python versions in your development environment. If you
use conda to install python use
tox-conda to automatically manage
testing across all supported python versions.
# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:
tox

Authors
See the included AUTHORS.md file for
more information.
License
This software is licensed under the BSD License.
See the included LICENSE.md file for
more information.
About
Serialize numpy arrays using msgpack
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

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var __re = new RegExp('^' + ".*" + '
GitHub - opentensor/msgpack-numpy: Serialize numpy arrays using msgpack · GitHub
Skip to content
BranchesTags
Repository files navigation
Numpy Data Type Serialization Using Msgpack
Package Description
This package provides encoding and decoding routines that enable the
serialization and deserialization of numerical and array data types provided by
numpy using the highly efficient
msgpack format. Serialization of Python's
native complex data types is also supported.
Latest VersionBuild Status
Installation
msgpack-numpy requires msgpack-python and numpy.
To install the package and all dependencies. You can download
the source tarball, unpack it, and run
python setup.py install

from within the source directory.
Usage
The easiest way to use msgpack-numpy is to call its monkey patching
function after importing the Python msgpack package:
import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization
routines (and other packages that use them) to become numpy-aware.
Of course, one can also manually pass the encoder and
decoder provided by msgpack-numpy to the msgpack routines:
import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.

If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON
variable is set), it will fall back to using the slower pure Python msgpack
implementation.
Notes
The primary design goal of msgpack-numpy is ensuring preservation of numerical
data types during msgpack serialization and deserialization. Inclusion of type
information in the serialized data necessarily incurs some storage overhead; if
preservation of type information is not needed, one may be able to avoid some
of this overhead by writing a custom encoder/decoder pair that produces more
efficient serializations for those specific use cases.
Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as
a fallback solution to enable msgpack-numpy to handle
such arrays. As the additional overhead of pickle serialization negates one
of the reasons to use msgpack, it may be advisable to either write a custom
encoder/decoder to handle the specific use case efficiently or else not bother
using msgpack-numpy.
Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied
if they are to be modified.
Development
The latest source code can be obtained from GitHub.
msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.
Install tox to support testing
across multiple python versions in your development environment. If you
use conda to install python use
tox-conda to automatically manage
testing across all supported python versions.
# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:
tox

Authors
See the included AUTHORS.md file for
more information.
License
This software is licensed under the BSD License.
See the included LICENSE.md file for
more information.
About
Serialize numpy arrays using msgpack
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
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Used by
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - opentensor/msgpack-numpy: Serialize numpy arrays using msgpack · GitHub
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Repository files navigation
Numpy Data Type Serialization Using Msgpack
Package Description
This package provides encoding and decoding routines that enable the
serialization and deserialization of numerical and array data types provided by
numpy using the highly efficient
msgpack format. Serialization of Python's
native complex data types is also supported.
Latest VersionBuild Status
Installation
msgpack-numpy requires msgpack-python and numpy.
To install the package and all dependencies. You can download
the source tarball, unpack it, and run
python setup.py install

from within the source directory.
Usage
The easiest way to use msgpack-numpy is to call its monkey patching
function after importing the Python msgpack package:
import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization
routines (and other packages that use them) to become numpy-aware.
Of course, one can also manually pass the encoder and
decoder provided by msgpack-numpy to the msgpack routines:
import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.

If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON
variable is set), it will fall back to using the slower pure Python msgpack
implementation.
Notes
The primary design goal of msgpack-numpy is ensuring preservation of numerical
data types during msgpack serialization and deserialization. Inclusion of type
information in the serialized data necessarily incurs some storage overhead; if
preservation of type information is not needed, one may be able to avoid some
of this overhead by writing a custom encoder/decoder pair that produces more
efficient serializations for those specific use cases.
Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as
a fallback solution to enable msgpack-numpy to handle
such arrays. As the additional overhead of pickle serialization negates one
of the reasons to use msgpack, it may be advisable to either write a custom
encoder/decoder to handle the specific use case efficiently or else not bother
using msgpack-numpy.
Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied
if they are to be modified.
Development
The latest source code can be obtained from GitHub.
msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.
Install tox to support testing
across multiple python versions in your development environment. If you
use conda to install python use
tox-conda to automatically manage
testing across all supported python versions.
# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:
tox

Authors
See the included AUTHORS.md file for
more information.
License
This software is licensed under the BSD License.
See the included LICENSE.md file for
more information.
About
Serialize numpy arrays using msgpack
Resources
Readme
License
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Stars
2 stars
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1 fork
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GitHub - opentensor/msgpack-numpy: Serialize numpy arrays using msgpack · GitHub
Skip to content
BranchesTags
Repository files navigation
Numpy Data Type Serialization Using Msgpack
Package Description
This package provides encoding and decoding routines that enable the
serialization and deserialization of numerical and array data types provided by
numpy using the highly efficient
msgpack format. Serialization of Python's
native complex data types is also supported.
Latest VersionBuild Status
Installation
msgpack-numpy requires msgpack-python and numpy.
To install the package and all dependencies. You can download
the source tarball, unpack it, and run
python setup.py install

from within the source directory.
Usage
The easiest way to use msgpack-numpy is to call its monkey patching
function after importing the Python msgpack package:
import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization
routines (and other packages that use them) to become numpy-aware.
Of course, one can also manually pass the encoder and
decoder provided by msgpack-numpy to the msgpack routines:
import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.

If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON
variable is set), it will fall back to using the slower pure Python msgpack
implementation.
Notes
The primary design goal of msgpack-numpy is ensuring preservation of numerical
data types during msgpack serialization and deserialization. Inclusion of type
information in the serialized data necessarily incurs some storage overhead; if
preservation of type information is not needed, one may be able to avoid some
of this overhead by writing a custom encoder/decoder pair that produces more
efficient serializations for those specific use cases.
Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as
a fallback solution to enable msgpack-numpy to handle
such arrays. As the additional overhead of pickle serialization negates one
of the reasons to use msgpack, it may be advisable to either write a custom
encoder/decoder to handle the specific use case efficiently or else not bother
using msgpack-numpy.
Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied
if they are to be modified.
Development
The latest source code can be obtained from GitHub.
msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.
Install tox to support testing
across multiple python versions in your development environment. If you
use conda to install python use
tox-conda to automatically manage
testing across all supported python versions.
# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:
tox

Authors
See the included AUTHORS.md file for
more information.
License
This software is licensed under the BSD License.
See the included LICENSE.md file for
more information.
About
Serialize numpy arrays using msgpack
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
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GitHub - opentensor/msgpack-numpy: Serialize numpy arrays using msgpack · GitHub
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Numpy Data Type Serialization Using Msgpack
Package Description
This package provides encoding and decoding routines that enable the
serialization and deserialization of numerical and array data types provided by
numpy using the highly efficient
msgpack format. Serialization of Python's
native complex data types is also supported.
Latest VersionBuild Status
Installation
msgpack-numpy requires msgpack-python and numpy.
To install the package and all dependencies. You can download
the source tarball, unpack it, and run
python setup.py install

from within the source directory.
Usage
The easiest way to use msgpack-numpy is to call its monkey patching
function after importing the Python msgpack package:
import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization
routines (and other packages that use them) to become numpy-aware.
Of course, one can also manually pass the encoder and
decoder provided by msgpack-numpy to the msgpack routines:
import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.

If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON
variable is set), it will fall back to using the slower pure Python msgpack
implementation.
Notes
The primary design goal of msgpack-numpy is ensuring preservation of numerical
data types during msgpack serialization and deserialization. Inclusion of type
information in the serialized data necessarily incurs some storage overhead; if
preservation of type information is not needed, one may be able to avoid some
of this overhead by writing a custom encoder/decoder pair that produces more
efficient serializations for those specific use cases.
Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as
a fallback solution to enable msgpack-numpy to handle
such arrays. As the additional overhead of pickle serialization negates one
of the reasons to use msgpack, it may be advisable to either write a custom
encoder/decoder to handle the specific use case efficiently or else not bother
using msgpack-numpy.
Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied
if they are to be modified.
Development
The latest source code can be obtained from GitHub.
msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.
Install tox to support testing
across multiple python versions in your development environment. If you
use conda to install python use
tox-conda to automatically manage
testing across all supported python versions.
# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:
tox

Authors
See the included AUTHORS.md file for
more information.
License
This software is licensed under the BSD License.
See the included LICENSE.md file for
more information.
About
Serialize numpy arrays using msgpack
Resources
Readme
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2 stars
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1 watching
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1 fork
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GitHub - opentensor/msgpack-numpy: Serialize numpy arrays using msgpack · GitHub
Skip to content
BranchesTags
Repository files navigation
Numpy Data Type Serialization Using Msgpack
Package Description
This package provides encoding and decoding routines that enable the
serialization and deserialization of numerical and array data types provided by
numpy using the highly efficient
msgpack format. Serialization of Python's
native complex data types is also supported.
Latest VersionBuild Status
Installation
msgpack-numpy requires msgpack-python and numpy.
To install the package and all dependencies. You can download
the source tarball, unpack it, and run
python setup.py install

from within the source directory.
Usage
The easiest way to use msgpack-numpy is to call its monkey patching
function after importing the Python msgpack package:
import msgpack
import msgpack_numpy as m
m.patch()

This will automatically force all msgpack serialization and deserialization
routines (and other packages that use them) to become numpy-aware.
Of course, one can also manually pass the encoder and
decoder provided by msgpack-numpy to the msgpack routines:
import msgpack
import msgpack_numpy as m
import numpy as np
x = np.random.rand(5)
x_enc = msgpack.packb(x, default=m.encode)
x_rec = msgpack.unpackb(x_enc, object_hook=m.decode)

msgpack-numpy will try to use the binary (fast) extension in msgpack by default.

If msgpack was not compiled with Cython (or if the MSGPACK_PUREPYTHON
variable is set), it will fall back to using the slower pure Python msgpack
implementation.
Notes
The primary design goal of msgpack-numpy is ensuring preservation of numerical
data types during msgpack serialization and deserialization. Inclusion of type
information in the serialized data necessarily incurs some storage overhead; if
preservation of type information is not needed, one may be able to avoid some
of this overhead by writing a custom encoder/decoder pair that produces more
efficient serializations for those specific use cases.
Numpy arrays with a dtype of 'O' are serialized/deserialized using pickle as
a fallback solution to enable msgpack-numpy to handle
such arrays. As the additional overhead of pickle serialization negates one
of the reasons to use msgpack, it may be advisable to either write a custom
encoder/decoder to handle the specific use case efficiently or else not bother
using msgpack-numpy.
Note that numpy arrays deserialized by msgpack-numpy are read-only and must be copied
if they are to be modified.
Development
The latest source code can be obtained from GitHub.
msgpack-numpy maintains compatibility with python versions 2.7 and 3.5+.
Install tox to support testing
across multiple python versions in your development environment. If you
use conda to install python use
tox-conda to automatically manage
testing across all supported python versions.
# Using a system python
pip install tox
# Additionally, using a conda-provided python
pip install tox tox-conda

Execute tests across supported python versions:
tox

Authors
See the included AUTHORS.md file for
more information.
License
This software is licensed under the BSD License.
See the included LICENSE.md file for
more information.
About
Serialize numpy arrays using msgpack
Resources
Readme
License
Activity
Custom properties
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages